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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Databricks Lakehouse Platform	24%	- Data Management - Unity Catalog - Delta Lake - Lakehouse Architecture
Data Processing	28%	- Spark SQL - Data Transformation - ETL Pipelines - Structured Streaming
Monitoring and Troubleshooting	16%	- Troubleshooting - Performance Optimization - Monitoring
Data Modeling and Storage	20%	- Data Modeling - File Formats - Storage Optimization
Data Quality and Governance	12%	- Data Lineage - Governance - Data Quality

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q115-Q120):

NEW QUESTION # 115
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

Answer: A

Explanation:
When joining a stream of advertisement impressions with a stream of user clicks, you want to minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime, which is suitable for correlating events that occur at the exact same time. However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.

NEW QUESTION # 116
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Give the analytics team direct access to the production table.
B. Create a new table for the analytics team using a CTAS statement.
C. Deep clone the table for the analytics team.
D. Shallow clone the table for the analytics team.

Answer: D

Explanation:
A shallow clone of the production Delta table creates an instantaneous snapshot that references the same data files, so it introduces virtually no downtime or storage overhead and avoids interfering with the ongoing ETL. A deep clone would copy all data (very expensive and slow for
20B rows). CTAS rewrites data and is unnecessary; direct access to prod risks contention and accidental changes.

NEW QUESTION # 117
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?

A. return spark.readStream.table("bronze")
B. return spark.readStream.load("bronze")
C. return spark.read.option("readChangeFeed", "true").table ("bronze")
D. def new_records():
E.

Answer: E

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html
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NEW QUESTION # 118
A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
B. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.
C. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

Answer: D

NEW QUESTION # 119
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
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Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
B. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
D. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
E. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.

Answer: D

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 120
......

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